Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm

2012 ◽  
Vol 56 (6) ◽  
pp. 1450-1467 ◽  
Author(s):  
F. Rigat ◽  
A. Mira
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
General Purpose ◽  
Monte Carlo Algorithm ◽  
Hierarchical Sampling

scholarly journals Nonreversible Markov Chain Monte Carlo Algorithm for Efficient Generation of Self-Avoiding Walks

2022 ◽  
Vol 9 ◽  
Author(s):  
Hanqing Zhao ◽  
Marija Vucelja
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Markov Chains ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Monte Carlo Algorithm ◽  
Efficient Generation ◽  
Self Avoiding Walk ◽  
Self Avoiding Walks

We introduce an efficient nonreversible Markov chain Monte Carlo algorithm to generate self-avoiding walks with a variable endpoint. In two dimensions, the new algorithm slightly outperforms the two-move nonreversible Berretti-Sokal algorithm introduced by H. Hu, X. Chen, and Y. Deng, while for three-dimensional walks, it is 3–5 times faster. The new algorithm introduces nonreversible Markov chains that obey global balance and allow for three types of elementary moves on the existing self-avoiding walk: shorten, extend or alter conformation without changing the length of the walk.

PENGEOM – A general-purpose geometry package for Monte Carlo simulation of radiation transport in complex material structures (New Version Announcement)

2021 ◽  
pp. 107962
Author(s):  
Julio Almansa ◽  
Francesc Salvat-Pujol ◽  
Gloria Díaz-Londoño ◽  
Artur Carnicer ◽  
Antonio M. Lallena ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Radiation Transport ◽  
General Purpose ◽  
Complex Material

Examining sharp restart in a Monte Carlo method for the linearized Poisson–Boltzmann equation

2020 ◽  
Vol 26 (3) ◽  
pp. 223-244
Author(s):  
W. John Thrasher ◽  
Michael Mascagni
Keyword(s):  
Monte Carlo ◽  
Free Energy ◽  
Monte Carlo Algorithm ◽  
Significant Bias ◽  
Poisson Boltzmann ◽  
Electrostatic Free Energy ◽  
Poisson Boltzmann Equation ◽  
The Stability ◽  
Potential Methods ◽  
The Individual

AbstractIt has been shown that when using a Monte Carlo algorithm to estimate the electrostatic free energy of a biomolecule in a solution, individual random walks can become entrapped in the geometry. We examine a proposed solution, using a sharp restart during the Walk-on-Subdomains step, in more detail. We show that the point at which this solution introduces significant bias is related to properties intrinsic to the molecule being examined. We also examine two potential methods of generating a sharp restart point and show that they both cause no significant bias in the examined molecules and increase the stability of the run times of the individual walks.

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